Single-cell mutational profiling and clonal phylogeny in cancer
Open accessAbstract
The development of cancer is a dynamic evolutionary process in which intraclonal, genetic diversity provides a substrate for clonal selection and a source of therapeutic escape. The complexity and topography of intraclonal genetic architectures have major implications for biopsy-based prognosis and for targeted therapy. High-depth, next-generation sequencing (NGS) efficiently captures the mutational load of individual tumors or biopsies. But, being a snapshot portrait of total DNA, it disguises the fundamental features of subclonal variegation of genetic lesions and of clonal phylogeny. Single-cell genetic profiling provides a potential resolution to this problem, but methods developed to date all have limitations. We present a novel solution to this challenge using leukemic cells with known mutational spectra as a tractable model. DNA from flow-sorted single cells is screened using multiplex targeted Q-PCR within a microfluidic platform allowing unbiased single-cell selection, high-throughput, and comprehensive analysis for all main varieties of genetic abnormalities: chimeric gene fusions, copy number alterations, and single-nucleotide variants. We show, in this proof-of-principle study, that the method has a low error rate and can provide detailed subclonal genetic architectures and phylogenies.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Experimental noise can strongly affect estimates of heterogeneity due to false positives, false negatives (including allelic dropouts), and data sparsity.
“Some of the challenges highlighted here, including experimental noise16,204, can strongly affect estimates of heterogeneity due to false positives, false negatives (including allelic dropouts77), and data sparsity; these will require new methods and adaptation of existing ones (e.g., in phylogenetic reconstruction205).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [204]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 52 · Cited by 107
- Authors
- Potter, N. E., Ermini, L., Papaemmanuil, E., Cazzaniga, G., Vijayaraghavan, G., Titley, I., Ford, A., Campbell, P., Kearney, L., & Greaves, M.
- Affiliations
- Institute of Cancer Research (GB)
- Wellcome Sanger Institute (GB)
- Azienda Ospedaliera San Gerardo (IT)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2013-09-20
- Volume
- 23
- Issue
- 12
- Pages
- 2115-2125
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3847780
- OpenAlex
- W2161055995
- Cited by
- 107
- References
- 52
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2022: 4 · 2021: 5 · 2020: 6 · 2019: 4 · 2018: 18 · 2017: 11 · 2016: 12 · 2015: 15 · 2014: 26
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion, cc-by-nc)
- Unicam Scientific Publications (University of Camerino) (submittedVersion, other-oa)
- Funders
- Kay Kendall Leukaemia Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Keywords
- intraclonal heterogeneity, tumor evolution, clonal selection, therapeutic escape, copy number alterations, single nucleotide variants, targeted therapy
- MeSH terms
- Humans, Mutation, Neoplasms, Phylogeny, Genetic Variation, Polymorphism, Single Nucleotide, Genomics, Cell Line, Tumor, DNA Copy Number Variations, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Multiplex Polymerase Chain Reaction, Clonal Evolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
